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概要と使いどころ
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
日本語の概要は準備中です。原文の説明を表示しています。
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text...
日本語の概要は準備中です。原文の説明を表示しています。
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. USE FOR: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, co...
日本語の概要は準備中です。原文の説明を表示しています。
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
日本語の概要は準備中です。原文の説明を表示しています。
Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns
日本語の概要は準備中です。原文の説明を表示しています。
Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.
日本語の概要は準備中です。原文の説明を表示しています。
Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.
日本語の概要は準備中です。原文の説明を表示しています。
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.
日本語の概要は準備中です。原文の説明を表示しています。
Monitors Certificate Transparency (CT) logs to detect unauthorized certificate issuance, discover subdomains via CT data, and alert on suspicious certificate activity for owned domains. Uses the crt.sh API and direct CT log querying based on RFC 6962 to build continuous monitoring pipelines that catch rogue certificates, track CA behavior, and map the external attack surface. Activates for requests involving certificate transparency monitoring, CT log auditing, subdomain discovery via certificates, or certificate issuance alerting.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.
日本語の概要は準備中です。原文の説明を表示しています。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
日本語の概要は準備中です。原文の説明を表示しています。
Uses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.
日本語の概要は準備中です。原文の説明を表示しています。
Complete SolanaOS agent skill — install, configure, and operate the autonomous Solana trading runtime with Honcho v3 epistemological memory, multi-venue perp trading (Hyperliquid + Aster), on-chain intelligence with USD pricing, Telegram bot, gateway API, Tailscale mesh, hardware integration, and cross-session recall. Use when asked to install SolanaOS, query Solana blockchain data, manage wallets, run OODA trading loops, configure strategies, control BitAxe mining fleets, pair Seeker devices, or operate any SolanaOS runtime surface.
日本語の概要は準備中です。原文の説明を表示しています。
Full SolanaOS operator skill — install the runtime, query Solana blockchain data via SolanaTracker RPC, manage wallets, run OODA trading loops, configure multi-venue strategies (spot + Hyperliquid + Aster perps), control BitAxe mining fleets, pair Seeker devices, and interact with the SolanaOS Hub. Use when asked about SolanaOS installation, Solana wallet balances, token research, trading strategy, OODA loops, gateway setup, Telegram bot configuration, mining fleet management, or any SolanaOS runtime operation.
日本語の概要は準備中です。原文の説明を表示しています。
Read-only query methods for PumpFun claims — unclaimed token rewards, creator vault balances, volume accumulators, distributable fees, and current-day token previews across Pump and PumpAMM programs.
日本語の概要は準備中です。原文の説明を表示しています。
Query Google Places API (New) via the goplaces CLI for text search, place details, resolve, and reviews. Use for human-friendly place lookup or JSON output for scripts.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Nuxt Content v3 - provides collections (local/remote/API sources), queryCollection API, MDC rendering, database configuration, NuxtStudio integration, hooks, i18n patterns, and LLMs integration
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when generating Salesforce CLI commands for tasks like authenticating to orgs, querying data with SOQL, retrieving metadata (profiles, permission sets, security settings), deploying configuration changes, or automating security audits. Use when the user describes what they want to accomplish with Salesforce and needs the specific CLI command syntax.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.
日本語の概要は準備中です。原文の説明を表示しています。
Author and submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)
日本語の概要は準備中です。原文の説明を表示しています。
Register a Snowflake table or view as a Narrative dataset from SQL, with no Configuration screen and no browser. Not for installing or configuring the native app, and not for querying a dataset that already exists. Use when: "register this table as a dataset", "register a Snowflake dataset from SQL", "onboard my_table to Narrative", "script dataset registration", "add this view to my data plane". (narrative-common)
日本語の概要は準備中です。原文の説明を表示しています。
Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)
日本語の概要は準備中です。原文の説明を表示しています。
I/O + 계산 혼재 또는 Mock stub + 계산 혼재 메서드를 Functional Core / Imperative Shell(빵속빵, Impure-Pure-Impure Sandwich)로 분리. "순수 함수 분리", "I/O와 계산 나눠", "mock 없이 테스트하게", "functional core", "/segregate-functional-core" 요청 시 사용. 단, 값 반환과 부수효과 분리(CQS)만이면 /separate-query-modifier가 적합. /segregate-functional-core [commit-ref]로 호출.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.
日本語の概要は準備中です。原文の説明を表示しています。